Medical Students’ Knowledge and Attitudes Toward Shared Decision Making: Results From a Multinational, Cross-Sectional Survey
Bibliographic record
Abstract
Introduction. We aimed to conduct a multinational cross-sectional online survey of medical students’ attitudes toward, knowledge of, and experience with shared decision making (SDM). Methods. We conducted the survey from September 2016 until May 2017 using the following: 1) a convenience sample of students from four medical schools each in Canada, the United States, and the Netherlands ( n = 12), and 2) all medical schools in the United Kingdom through the British Medical School Council ( n = 32). We also distributed the survey through social media. Results. A total of 765 students read the information sheet and 619 completed the survey. Average age was 24, 69% were female. Mean SDM knowledge score was 83.6% (range = 18.8% to 100%; 95% confidence interval [CI] = 82.8% to 84.5%). US students had the highest knowledge scores (86.2%, 95% CI = 84.8% to 87.6%). The mean risk communication score was 57.4% (range = 0% to 100%; 95% CI = 57.4% to 60.1%). Knowledge did not vary with age, race, gender, school, or school year. Attitudes were positive, except 46% believed SDM could only be done with higher educated patients, and 80.9% disagreed that physician payment should be linked to SDM performance (increased with years in training, P < 0.05). Attitudes did not vary due to any tested variable. Students indicated they were more likely than experienced clinicians to practice SDM (72.1% v. 48.8%). A total of 74.7% reported prior SDM training and 82.8% were interested in learning more about SDM. Discussion. SDM knowledge is high among medical students in all four countries. Risk communication is less well understood. Attitudes indicate that further research is needed to understand how medical schools deliver and integrate SDM training into existing curricula.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".